## [1] "explicated variable of regression : rh98"
## [1] "for Guinean_forest-savanna_regression_rh98.RDS"
## [2] "for Northern_Congolian_Forest-Savanna_regression_rh98.RDS"
## [3] "for Sahelian_Acacia_savanna_regression_rh98.RDS"
## [4] "for Southern_Congolian_forest-savanna_regression_rh98.RDS"
## [5] "for West_Sudanian_savanna_regression_rh98.RDS"
## [6] "for Western_Congolian_forest-savanna_regression_rh98.RDS"
## [1] "########################################"
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## [1] "below, stancode for Guinean_forest-savanna_regression_rh98.RDS"
## // generated with brms 2.20.4
## functions {
## }
## data {
## int<lower=1> N; // total number of observations
## vector[N] Y; // response variable
## int<lower=1> K; // number of population-level effects
## matrix[N, K] X; // population-level design matrix
## int<lower=1> Kc; // number of population-level effects after centering
## int prior_only; // should the likelihood be ignored?
## }
## transformed data {
## matrix[N, Kc] Xc; // centered version of X without an intercept
## vector[Kc] means_X; // column means of X before centering
## for (i in 2:K) {
## means_X[i - 1] = mean(X[, i]);
## Xc[, i - 1] = X[, i] - means_X[i - 1];
## }
## }
## parameters {
## vector[Kc] b; // regression coefficients
## real Intercept; // temporary intercept for centered predictors
## real<lower=0> shape; // shape parameter
## }
## transformed parameters {
## real lprior = 0; // prior contributions to the log posterior
## lprior += student_t_lpdf(Intercept | 3, 2, 2.5);
## lprior += gamma_lpdf(shape | 0.01, 0.01);
## }
## model {
## // likelihood including constants
## if (!prior_only) {
## // initialize linear predictor term
## vector[N] mu = rep_vector(0.0, N);
## mu += Intercept + Xc * b;
## mu = exp(mu);
## target += gamma_lpdf(Y | shape, shape ./ mu);
## }
## // priors including constants
## target += lprior;
## }
## generated quantities {
## // actual population-level intercept
## real b_Intercept = Intercept - dot_product(means_X, b);
## }
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## [1] " "
## [1] " "
## [1] "Guinean_forest-savanna_regression_rh98.RDS"
## [1] "########################################"
## Family: gamma
## Links: mu = log; shape = identity
## Formula: rh98 ~ fire_freq_std + mean_precip_std
## Data: table_region (Number of observations: 1725)
## Draws: 4 chains, each with iter = 10000; warmup = 2000; thin = 10;
## total post-warmup draws = 3200
##
## Population-Level Effects:
## Estimate Est.Error l-95% CI u-95% CI Rhat Bulk_ESS Tail_ESS
## Intercept 1.96 0.03 1.91 2.02 1.00 3075 3028
## fire_freq_std 0.03 0.01 0.01 0.05 1.00 3072 3045
## mean_precip_std 0.06 0.01 0.04 0.09 1.00 3046 3023
##
## Family Specific Parameters:
## Estimate Est.Error l-95% CI u-95% CI Rhat Bulk_ESS Tail_ESS
## shape 3.80 0.12 3.56 4.04 1.00 3076 3171
##
## Draws were sampled using sampling(NUTS). For each parameter, Bulk_ESS
## and Tail_ESS are effective sample size measures, and Rhat is the potential
## scale reduction factor on split chains (at convergence, Rhat = 1).
## [1] "########################################"
## [1] "Northern_Congolian_Forest-Savanna_regression_rh98.RDS"
## [1] "########################################"
## Family: gamma
## Links: mu = log; shape = identity
## Formula: rh98 ~ fire_freq_std + mean_precip_std
## Data: table_region (Number of observations: 243)
## Draws: 4 chains, each with iter = 10000; warmup = 2000; thin = 10;
## total post-warmup draws = 3200
##
## Population-Level Effects:
## Estimate Est.Error l-95% CI u-95% CI Rhat Bulk_ESS Tail_ESS
## Intercept 2.13 0.19 1.76 2.49 1.00 3075 3089
## fire_freq_std -0.03 0.03 -0.09 0.04 1.00 3042 3003
## mean_precip_std -0.00 0.09 -0.17 0.17 1.00 3065 3131
##
## Family Specific Parameters:
## Estimate Est.Error l-95% CI u-95% CI Rhat Bulk_ESS Tail_ESS
## shape 5.01 0.45 4.15 5.91 1.00 3021 3166
##
## Draws were sampled using sampling(NUTS). For each parameter, Bulk_ESS
## and Tail_ESS are effective sample size measures, and Rhat is the potential
## scale reduction factor on split chains (at convergence, Rhat = 1).
## [1] "########################################"
## [1] "Sahelian_Acacia_savanna_regression_rh98.RDS"
## [1] "########################################"
## Family: gamma
## Links: mu = log; shape = identity
## Formula: rh98 ~ fire_freq_std + mean_precip_std
## Data: table_region (Number of observations: 5563)
## Draws: 4 chains, each with iter = 10000; warmup = 2000; thin = 10;
## total post-warmup draws = 3200
##
## Population-Level Effects:
## Estimate Est.Error l-95% CI u-95% CI Rhat Bulk_ESS Tail_ESS
## Intercept 1.32 0.01 1.31 1.33 1.00 3297 3124
## fire_freq_std 0.11 0.01 0.10 0.12 1.00 3220 3216
## mean_precip_std 0.33 0.01 0.31 0.35 1.00 3299 3134
##
## Family Specific Parameters:
## Estimate Est.Error l-95% CI u-95% CI Rhat Bulk_ESS Tail_ESS
## shape 18.01 0.34 17.34 18.69 1.00 3452 3136
##
## Draws were sampled using sampling(NUTS). For each parameter, Bulk_ESS
## and Tail_ESS are effective sample size measures, and Rhat is the potential
## scale reduction factor on split chains (at convergence, Rhat = 1).
## [1] "########################################"
## [1] "Southern_Congolian_forest-savanna_regression_rh98.RDS"
## [1] "########################################"
## Family: gamma
## Links: mu = log; shape = identity
## Formula: rh98 ~ fire_freq_std + mean_precip_std
## Data: table_region (Number of observations: 47)
## Draws: 4 chains, each with iter = 10000; warmup = 2000; thin = 10;
## total post-warmup draws = 3200
##
## Population-Level Effects:
## Estimate Est.Error l-95% CI u-95% CI Rhat Bulk_ESS Tail_ESS
## Intercept 3.37 0.65 2.10 4.68 1.00 3137 3092
## fire_freq_std -0.08 0.04 -0.16 0.01 1.00 2968 3183
## mean_precip_std -0.96 0.31 -1.57 -0.36 1.00 3195 3092
##
## Family Specific Parameters:
## Estimate Est.Error l-95% CI u-95% CI Rhat Bulk_ESS Tail_ESS
## shape 6.07 1.25 3.88 8.75 1.00 3131 2904
##
## Draws were sampled using sampling(NUTS). For each parameter, Bulk_ESS
## and Tail_ESS are effective sample size measures, and Rhat is the potential
## scale reduction factor on split chains (at convergence, Rhat = 1).
## [1] "########################################"
## [1] "West_Sudanian_savanna_regression_rh98.RDS"
## [1] "########################################"
## Family: gamma
## Links: mu = log; shape = identity
## Formula: rh98 ~ fire_freq_std + mean_precip_std
## Data: table_region (Number of observations: 3277)
## Draws: 4 chains, each with iter = 10000; warmup = 2000; thin = 10;
## total post-warmup draws = 3200
##
## Population-Level Effects:
## Estimate Est.Error l-95% CI u-95% CI Rhat Bulk_ESS Tail_ESS
## Intercept 1.22 0.02 1.19 1.25 1.00 3067 3114
## fire_freq_std 0.08 0.01 0.07 0.10 1.00 3044 3056
## mean_precip_std 0.53 0.02 0.50 0.56 1.00 3052 2874
##
## Family Specific Parameters:
## Estimate Est.Error l-95% CI u-95% CI Rhat Bulk_ESS Tail_ESS
## shape 5.47 0.13 5.21 5.73 1.00 3600 2875
##
## Draws were sampled using sampling(NUTS). For each parameter, Bulk_ESS
## and Tail_ESS are effective sample size measures, and Rhat is the potential
## scale reduction factor on split chains (at convergence, Rhat = 1).
## [1] "########################################"
## [1] "Western_Congolian_forest-savanna_regression_rh98.RDS"
## [1] "########################################"
## Family: gamma
## Links: mu = log; shape = identity
## Formula: rh98 ~ fire_freq_std + mean_precip_std
## Data: table_region (Number of observations: 259)
## Draws: 4 chains, each with iter = 10000; warmup = 2000; thin = 10;
## total post-warmup draws = 3200
##
## Population-Level Effects:
## Estimate Est.Error l-95% CI u-95% CI Rhat Bulk_ESS Tail_ESS
## Intercept 1.36 0.16 1.05 1.66 1.00 2982 3193
## fire_freq_std -0.05 0.02 -0.10 -0.00 1.00 2648 2861
## mean_precip_std 0.36 0.10 0.18 0.56 1.00 3026 3172
##
## Family Specific Parameters:
## Estimate Est.Error l-95% CI u-95% CI Rhat Bulk_ESS Tail_ESS
## shape 3.07 0.26 2.61 3.60 1.00 3508 3132
##
## Draws were sampled using sampling(NUTS). For each parameter, Bulk_ESS
## and Tail_ESS are effective sample size measures, and Rhat is the potential
## scale reduction factor on split chains (at convergence, Rhat = 1).
## [1] "########################################"
## [1] "Gamma regressions for Guinean_forest-savanna_regression_rh98.RDS"
## [2] "Gamma regressions for Northern_Congolian_Forest-Savanna_regression_rh98.RDS"
## [3] "Gamma regressions for Sahelian_Acacia_savanna_regression_rh98.RDS"
## [4] "Gamma regressions for Southern_Congolian_forest-savanna_regression_rh98.RDS"
## [5] "Gamma regressions for West_Sudanian_savanna_regression_rh98.RDS"
## [6] "Gamma regressions for Western_Congolian_forest-savanna_regression_rh98.RDS"
## [1] "########################################"
## [1] "########################################"
## [1] "########################################"
## [1] "Guinean_forest-savanna_regression_rh98.RDS"
## [1] "########################################"
## [1] "dim(linear_predictors_for_one_beta_draw_per_column)"
## [1] 1725 3200
## [1] "(nb_donnes I * nb_iter_mcmc J)"
## [1] "mean(table_region$rh98)"
## [1] 8.17064
## [1] "sd(table_region$rh98)"
## [1] 4.228092
## [1] "mean(simulations[,j]) ( truth = 8.171 )"
## [1] 8.018
## [1] "sd(simulations[,j]) ( truth = 4.228 )"
## [1] 4.136
## [1] "mean(simulations[,j]) ( truth = 8.171 )"
## [1] 8.242
## [1] "sd(simulations[,j]) ( truth = 4.228 )"
## [1] 4.081
## [1] "mean(simulations[,j]) ( truth = 8.171 )"
## [1] 8.107
## [1] "sd(simulations[,j]) ( truth = 4.228 )"
## [1] 4.296
## [1] "mean(simulations[,j]) ( truth = 8.171 )"
## [1] 8.124
## [1] "sd(simulations[,j]) ( truth = 4.228 )"
## [1] 4.023
## [1] "mean(simulations[,j]) ( truth = 8.171 )"
## [1] 8.254
## [1] "sd(simulations[,j]) ( truth = 4.228 )"
## [1] 4.377
## [1] "Northern_Congolian_Forest-Savanna_regression_rh98.RDS"
## [1] "########################################"
## [1] "dim(linear_predictors_for_one_beta_draw_per_column)"
## [1] 243 3200
## [1] "(nb_donnes I * nb_iter_mcmc J)"
## [1] "mean(table_region$rh98)"
## [1] 8.251706
## [1] "sd(table_region$rh98)"
## [1] 3.52155
## [1] "mean(simulations[,j]) ( truth = 8.252 )"
## [1] 7.967
## [1] "sd(simulations[,j]) ( truth = 3.522 )"
## [1] 3.848
## [1] "mean(simulations[,j]) ( truth = 8.252 )"
## [1] 8.133
## [1] "sd(simulations[,j]) ( truth = 3.522 )"
## [1] 3.79
## [1] "mean(simulations[,j]) ( truth = 8.252 )"
## [1] 7.529
## [1] "sd(simulations[,j]) ( truth = 3.522 )"
## [1] 3.301
## [1] "mean(simulations[,j]) ( truth = 8.252 )"
## [1] 8.149
## [1] "sd(simulations[,j]) ( truth = 3.522 )"
## [1] 3.831
## [1] "mean(simulations[,j]) ( truth = 8.252 )"
## [1] 8.477
## [1] "sd(simulations[,j]) ( truth = 3.522 )"
## [1] 3.885
## [1] "Sahelian_Acacia_savanna_regression_rh98.RDS"
## [1] "########################################"
## [1] "dim(linear_predictors_for_one_beta_draw_per_column)"
## [1] 5563 3200
## [1] "(nb_donnes I * nb_iter_mcmc J)"
## [1] "mean(table_region$rh98)"
## [1] 3.049537
## [1] "sd(table_region$rh98)"
## [1] 1.187586
## [1] "mean(simulations[,j]) ( truth = 3.05 )"
## [1] 3.036
## [1] "sd(simulations[,j]) ( truth = 1.188 )"
## [1] 0.974
## [1] "mean(simulations[,j]) ( truth = 3.05 )"
## [1] 3.032
## [1] "sd(simulations[,j]) ( truth = 1.188 )"
## [1] 0.987
## [1] "mean(simulations[,j]) ( truth = 3.05 )"
## [1] 3.033
## [1] "sd(simulations[,j]) ( truth = 1.188 )"
## [1] 0.993
## [1] "mean(simulations[,j]) ( truth = 3.05 )"
## [1] 3.036
## [1] "sd(simulations[,j]) ( truth = 1.188 )"
## [1] 0.978
## [1] "mean(simulations[,j]) ( truth = 3.05 )"
## [1] 3.06
## [1] "sd(simulations[,j]) ( truth = 1.188 )"
## [1] 0.989
## [1] "Southern_Congolian_forest-savanna_regression_rh98.RDS"
## [1] "########################################"
## [1] "dim(linear_predictors_for_one_beta_draw_per_column)"
## [1] 47 3200
## [1] "(nb_donnes I * nb_iter_mcmc J)"
## [1] "mean(table_region$rh98)"
## [1] 3.938866
## [1] "sd(table_region$rh98)"
## [1] 2.086246
## [1] "mean(simulations[,j]) ( truth = 3.939 )"
## [1] 3.701
## [1] "sd(simulations[,j]) ( truth = 2.086 )"
## [1] 1.437
## [1] "mean(simulations[,j]) ( truth = 3.939 )"
## [1] 4.2
## [1] "sd(simulations[,j]) ( truth = 2.086 )"
## [1] 2.312
## [1] "mean(simulations[,j]) ( truth = 3.939 )"
## [1] 4.456
## [1] "sd(simulations[,j]) ( truth = 2.086 )"
## [1] 2.167
## [1] "mean(simulations[,j]) ( truth = 3.939 )"
## [1] 4.478
## [1] "sd(simulations[,j]) ( truth = 2.086 )"
## [1] 2.204
## [1] "mean(simulations[,j]) ( truth = 3.939 )"
## [1] 3.967
## [1] "sd(simulations[,j]) ( truth = 2.086 )"
## [1] 2.352
## [1] "West_Sudanian_savanna_regression_rh98.RDS"
## [1] "########################################"
## [1] "dim(linear_predictors_for_one_beta_draw_per_column)"
## [1] 3277 3200
## [1] "(nb_donnes I * nb_iter_mcmc J)"
## [1] "mean(table_region$rh98)"
## [1] 5.728088
## [1] "sd(table_region$rh98)"
## [1] 3.15554
## [1] "mean(simulations[,j]) ( truth = 5.728 )"
## [1] 5.754
## [1] "sd(simulations[,j]) ( truth = 3.156 )"
## [1] 3.108
## [1] "mean(simulations[,j]) ( truth = 5.728 )"
## [1] 5.734
## [1] "sd(simulations[,j]) ( truth = 3.156 )"
## [1] 3.079
## [1] "mean(simulations[,j]) ( truth = 5.728 )"
## [1] 5.776
## [1] "sd(simulations[,j]) ( truth = 3.156 )"
## [1] 3.169
## [1] "mean(simulations[,j]) ( truth = 5.728 )"
## [1] 5.797
## [1] "sd(simulations[,j]) ( truth = 3.156 )"
## [1] 3.108
## [1] "mean(simulations[,j]) ( truth = 5.728 )"
## [1] 5.748
## [1] "sd(simulations[,j]) ( truth = 3.156 )"
## [1] 3.142
## [1] "Western_Congolian_forest-savanna_regression_rh98.RDS"
## [1] "########################################"
## [1] "dim(linear_predictors_for_one_beta_draw_per_column)"
## [1] 259 3200
## [1] "(nb_donnes I * nb_iter_mcmc J)"
## [1] "mean(table_region$rh98)"
## [1] 6.137749
## [1] "sd(table_region$rh98)"
## [1] 4.607916
## [1] "mean(simulations[,j]) ( truth = 6.138 )"
## [1] 6.526
## [1] "sd(simulations[,j]) ( truth = 4.608 )"
## [1] 4.168
## [1] "mean(simulations[,j]) ( truth = 6.138 )"
## [1] 6.45
## [1] "sd(simulations[,j]) ( truth = 4.608 )"
## [1] 4.353
## [1] "mean(simulations[,j]) ( truth = 6.138 )"
## [1] 7.079
## [1] "sd(simulations[,j]) ( truth = 4.608 )"
## [1] 5.042
## [1] "mean(simulations[,j]) ( truth = 6.138 )"
## [1] 5.607
## [1] "sd(simulations[,j]) ( truth = 4.608 )"
## [1] 3.163
## [1] "mean(simulations[,j]) ( truth = 6.138 )"
## [1] 6.369
## [1] "sd(simulations[,j]) ( truth = 4.608 )"
## [1] 3.703
